Exam Questions Flashcards
Exam Topics
Describe Fundamental Traffic Flow Equation
The fundamental equation of Traffic Flow is:-
q= k ῡs
Where q = vehicle flow rate (veh/h.) It is the equivalent hourly rate at which vehicles pass a point in the highway
k = density (concentration) (veh/km). It is the number of vehicles which occupy a unit length of highway ay an instant in time.
ῡs= space mean speed (km/h.). The space mean speed is the harmonic mean of the speeds of vehicles passing a point of a highway during an interval of time. (3 marks)
The relationship is graphically represented by the fundamental diagram of traffic flow below:
Show the relationship changes as traaffic conditions change from uncongested to congested
When the highway is devoid of traffic, the density and the flow are both zero. There is little interaction of vehicles and thus the space mean speed is the maximum speed that can be attained. This is designated as uf. The flow in thus uncongested.
As flow increases, so does the density and speed declines. When flow reaches its maximum, known as qmax, the highway is at its capacity. Further increases in density lead to a decrease in flow and a further reduction in speed. The flow is now congested. As density increases to its maximum flow and speed decrease to zero and the highway is jammed. This maximum or “jam density” is designated kj
The slope of the flow-density curve is the space mean speed at that density.
How to determine elements of traffic management plan
The traffic management plan should thus consider all the forms of activity in the area, establish a priority of usage of existing facilities and develop short term implementable measures to make the most efficient usage of existing streets. (4 Marks)
Based on the circumstances, a traffic management plan can have some or all of the following elements:-
(a) Measures to influence time and place of trip generation e.g staggering work hours, traffic restraint schemes;
(b) Measures to influence the choice of mode e.g. public transport improvements, congestion pricing, park & ride, Bicycle lanes
(c) Measures to influence route choice e.g. traffic signal co-ordination, channelisation;
(d) Street Usage allocation e.g. one-way systems, HOV lanes, pedestrianisation;
(e) Junction control e.g. roundabouts, restricted turns
(f) Parking controls e.g. on/off street parking schemes, time of day parking restrictions;
(g) Measures to influence safety e.g. speed bumps, traffic calming, pedestrian crossings
(h) Measures aimed at environmental problems e.g. signal co-ordination to minimize overall emissions .
Tools in the traffic management plan
The tools available to traffic authorities in the implementation of these measures would be:-
(a) traffic regulations and legislation
(b) enforcement of regulations
(c) traffic control measures e.g. signals, road markings
(d) engineering construction e.g widening of junctions, channelisation, construction of loading bays.
Aim of a traffic management Plan
The aim of a traffic management plan is to make better and more efficient use of the existing transportation infrastructure. Such a use should also consider safety, environmental and energy consumption considerations. A good traffic management plan thus starts with a review of the land usage that impacts on the demand for transportation services and infrastructure. It would require the collection of data on the transportation infrastructure and services as well as an analysis of the current performance of the transportation system within the study area. The plans should consider the needs of key stakeholders and identify the most vulnerable users and affected non-users. Stakeholders would include local government representatives, merchants, shoppers, employees, school children pedestrians & commuters who need to traverse the area to and from points beyond.
The traffic management plan should thus consider all the forms of activity in the area, establish a priority of usage of existing facilities and develop short term implementable measures to make the most efficient usage of existing streets
Quality Control
Sampling by Attributes Using Binomial Distribution (3 marks) • Attribute data is the simplest kind. • You sample some number of items and you classify each item as either having some attribute, like being defective, or not. • The attribute plan decision rule will reject if too many points are “out”. Typically, the maximum number of defectives allowed in the sample is calculated with the binomial distribution. • Uses Binomial Distribution
Sampling by Variables Using Normal Distribution (3 marks)
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• Variables data contains more information than attribute data per data-point. This is because it allows you to assess “how much” or “how bad” or “how good” rather than just “yes its defective” or “no its not defective”. • Uses the original measurements. • The variables plan decision rule will reject if the sample average of the measurements goes outside of some calculated acceptable range. Typically, the limits of that acceptable range are calculated with the normal distribution. • Uses Normal Distribution.
Other Quality Control
CONTROL OF METHODS Method control is usually carried out by the Consultant`s field staff whose job it is to be on the site and supervise the Contractor during the execution of the works. At the same time the field staff will perform simple measurements, such as the recording of the thickness of fill layers, the temperature of asphalt material, and the slump of cement concrete. Method control is carried out according to the type of work. Where the work method is of considerable importance and requires constant supervision to achieve the quality, or where in some case, the quality is difficult to improve on, there should always be a field engineer on the site. Examples are the ramming of piles, the laying of asphalt, and concreting etc. Where work methods are of less importance or quality is constantly being achieved by the contractor, there may be no need for continuous surveillance. (4 marks)
- CONTROL OF END-RESULTS End-result control includes field tests e.g. control of the evenness of completed pavement layers and laboratory tests, e.g. Marshall tests on asphalt materials. Other tests are a combination of field and laboratory tests. An example of this is the compaction control of earthworks where the achieved density is determined by means of a field test, and where the IS/ AASHTO density with which the result should be compared is found by means of a laboratory test. End results control is carried out by laboratory technicians, and most of the work consists of laboratory tests. The frequency of end-result control depends on the quality parameters that are to be checked. Parameters which can vary considerably are continuously controlled. Examples are the composition of asphalt materials and the compaction of asphalt courses. As regards regulating laboratory tests the specification usually determines the number of tests. When the works are started and in cases where difficulties as regards compliance with quality requirements are encountered, laboratory testing will normally be intensified.
Outline steps in the transportation process and show the role it plays in the improvement o=to mobility across transport zone
Trip Generation
Trip Distribtuion
Modal Choice
Modal Choice
Modal Choice.
The purpose of a modal choice model is to predict the trip maker’s choice of travel mode. These models are usually applied after the trip distribution and are term trip-interchange models. The independent variables affecting modal choice are
(i) socio-economic characteristics of the trip-maker e.g. gender, income, age , car ownership
(ii) the attributes of the available modes e.g. cost, waiting time, in-vehicle travel time, safety, comfort and
(ii) the characteristics of the trip e.g. time of day, purpose. The multinomial logit formulation is the most popular probabilistic modal choice model.
The functional form is:- P(k) = eUk/∑eUm
where
P(k) = proportion of travelers choosing mode (k)
m = 1…nth mode
Uk = ak + b1X1 + b2X2 ..+bnXn
Where:
Uk = utility of mode K,
ak = calibrated mode specific constant
bi = parameter weighting the ith independent variable
Trip Distribution
The objective of the trip distribution model is to estimate the target-year trip interchange volumes between all zone pairs. The trip productions from each zone I from the trip generation phase are distributed among the trip-attracting zones J. The trip volume from zone I that a zone J would attract depends on its relative attractiveness and the relative impedance between the producing zone I and the attracting zone J. Proxy variables for attractiveness include employment (for work trips) school places (for school trips) and square area for retail shopping (shopping trips). Impedance can be measured by travel time, travel cost or a generalized travel disutility function incorporating both time and cost elements. The most common functional from is the Gravity Model thus:
TIJ = PI [ AJFij]
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∑ AxFix x
where:- TIJ = trip interchange volume between zones I and J PI = Trips generated from zone I AJ = Attractiveness of zone J FIJ = impedance between zones I and J
Trip Generation
The objective of a trip generation model is to forecast the number of person-trips or vehicle-trips that will begin from or end in each travel analysis zone within the region for a typical day of the target year. Then total number of trips generated is the dependent variable. The independent variables include land use (square area allocated to various activities), income, car ownership, employment status and other socio-economic characteristics. A typical functional form is a multiple regression model.
Y = a + b1X1+ b2X2 ….+ bnXn
Where Y = dependent variable
a = constant
bi = “weight’ of the ith independent variable
Xi = ith independenth variable.
